GENOMIC SUBTYPES OF HEPATOCELLULAR CARCINOMA ASSOCIATED WITH CLINICAL OUTCOMES

Abstract

Background: While many studies revealed transcriptomic subtypes of hepatocellular carcinoma (HCC), they are not translated to the clinic yet due to lack of consensus. We aim to examine consensus of transcriptomic subtypes and uncover their clinical significance.

Methods: We integrated 16 previously established transcriptomic signatures for HCC to uncover consensus subtypes. We also developed and validated a robust predictor of consensus subtype with 100 genes (PICS100). Informatics and statistics approaches were applied to find clinical relevant association of genomic features. Patient derived xenograft (PDX) models were used for testing hypothesis from analysis of transcriptomic data.

Results: We identified 5 clinically and molecularly distinct consensus subtypes. STM (STeM) is characterized by high stem cell features, vascular invasion, and poor prognosis. CIN (Chromosomal INstability) has moderate stem cell features but high genomic instability and low immune activity. High expression of IGF2 is another unique feature of CIN subtype, suggesting that CIN subtype might have benefit of treatment targeting IGF2/IGFR pathway. IMH (IMmune High) is best characterized by its high TCR diversity and high baseline immune activity. BCM (Beta-Catenin with high Male predominance) is characterized by prominent b-catenin activation, low miRNA expression, hypomethylation, and high sensitivity to sorafenib. Interestingly, subtype BCM had a significantly higher male-to-female ratio than the other subtypes. Another unique molecular characteristic of subtype D was miRNA downregulation. DLP (Differentiated and Low Proliferation) is differentiated with high HNF4A activity. We also identified potential serum biomarkers that can stratify patients into 5 subtypes. Our PICS100 predictor is available in the website (https://kasaha1.shinyapps.io/pics100/) with test data set for those who wish to run genomic predictor.

Conclusion: Five HCC subtypes are associated with potential response to treatments and highly conserved in pre-clinical models, providing a framework for selecting the most appropriate models for preclinical studies of new drugs and potentially for future clinical trials.